A Unified Framework for Large Scale Scientific Computing

大规模科学计算的统一框架

基本信息

批准号：
0727600
负责人：
Jun Zhang
金额：
$ 19万
依托单位：
University of Kentucky Research Foundation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2007
资助国家：
美国
起止时间：
2007-10-01 至 2011-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0727600&HistoricalAwards=false
关键词：
Unified Framework Large Scale Scientific

项目摘要

This research builds a unified computational framework for scalable and high efficiency solution of elliptic partial differential equations. The investigators develop a novel high-order multiscale multigrid computation methodology, which combines high accuracy computation and fast computing methods in a seamless way. This research work may impact many computational science and engineering and industry modeling and simulation applications. As U.S. high-tech industry moves from experiment-based design and development to computer-assisted design and development, higher performance numerical methods and faster computer simulation techniques will benefit U.S. industry by enabling design and development engineers to conduct quick verification to test their new ideas on computers, before committing to expensive experiments. These technologies are essential for the U.S. industry to maintain its leadership position in the competitive world market. Graduate students, including members from underrepresented groups, are trained to become the next generation researchers and educators with solid scientific computing skills. The technique simultaneously advances the numerical solution of partial differential equations in two fronts. One is to compute high accuracy solution by using high-order discretization methods, another is to compute the discrete solution in a minimum amount of computer time by using the fastest sparse linear system solvers. This unified framework advances the two fronts collectively by fusing the ideas and advantages of multiscale discretization and multigrid computations, to achieve the ultimate goal of computing accurate numerical solution at the minimum computer costs. It is the convergence of years of research work by many researchers in several different areas. This computational framework possesses high accuracy, high speed, high scalability, and delivers optimal efficiency for computing the numerical solution of elliptic partial differential equations.

这项研究构建了一个统一的计算框架，用于椭圆偏微分方程的可扩展和高效求解。研究人员开发了一种新颖的高阶多尺度多重网格计算方法，该方法以无缝方式结合了高精度计算和快速计算方法。这项研究工作可能会影响许多计算科学和工程以及工业建模和仿真应用。随着美国高科技产业从基于实验的设计和开发转向计算机辅助设计和开发，更高性能的数值方法和更快的计算机模拟技术将使设计和开发工程师能够快速验证以测试他们的新想法，从而使美国工业受益在进行昂贵的实验之前，先在计算机上进行研究。这些技术对于美国工业在竞争激烈的世界市场中保持领先地位至关重要。研究生，包括来自代表性不足群体的成员，接受培训，成为具有扎实科学计算技能的下一代研究人员和教育工作者。该技术同时在两个方面推进了偏微分方程的数值求解。一种是使用高阶离散化方法计算高精度解，另一种是使用最快的稀疏线性系统求解器在最短的计算机时间内计算离散解。这个统一的框架通过融合多尺度离散化和多重网格计算的思想和优点，共同推进了两个前沿，以达到以最小的计算机成本计算精确数值解的最终目标。它是多个不同领域的许多研究人员多年研究工作的汇集。该计算框架具有高精度、高速度、高可扩展性，为椭圆偏微分方程数值解的计算提供了最佳效率。